Bridging the Valley of Death for Geospatial Models

The valley of death (VoD) phenomenon is well known in all industries, where promising research only stays promising in the labs and falls apart for commercial purposes. In geospatial AI, this gap is particularly pronounced, leaving billions of dollars in potential value unrealized and critical global challenges unaddressed.

The reason is simple: Commercial demands are vastly different from the perfectly controlled environment in research. When models are taken out to the real world, they fail to handle the messiness of real world data and the strict demands of business requirements.

Research Context vs. Production Reality

Here’s a comparative table of the different contexts that explains why so many fail to climb out of the valley of death.

	Research Context	Production Context
Problem Scope	Extremely narrow	Broader and often cross-disciplinary
Mandate	Does not have to be economically viable	Must be economically viable
Allocation of Resources	Depends on funding and department, but usually more forgiving.	Restricted, costs must make sense given the business value.
Dataset Availability	Widely available	Oftentimes non-existent, especially for data scarce regions and new parameters.
Dataset Quality	Curated, consistent, carefully pre-processed datasets.	Real-world data is often incomplete, distorted from interferences, and aren’t standardized in terms of how they’re collected and metadata.
Margin of Error	Variable and flexible, will only affect conclusion made	Strict. Businesses need a high level of reliability for reputation
Usage	Limited, sample sizes are usually very small	Very broad and diverse, increasing the chance for errors
Change over Time	Practically none. Once research is concluded, it does not evolve.	Dynamic. Business needs, markets, and technologies evolve constantly.

Why Most Attempts Fail

Underestimating Engineering Complexity

Research teams often assume that getting a model to work in a notebook means it's ready for production. The reality is that production engineering typically requires multitudes more effort than the initial research.

Not Considering Business Requirements

Technical teams focus on model accuracy while business stakeholders care about ROI, reliability, and integration with existing workflows. Success requires understanding both technical and business constraints from day one.

Insufficient Infrastructure Investment

Many organizations try to deploy production AI systems on research-grade infrastructure, leading to reliability and scalability problems that destroy user trust.

Lack of Domain Expertise

Successful geospatial AI production systems require deep understanding of both the technical methods and the application domain: Agriculture, forestry, urban planning, etc., as well as the builders for technical capacity: Engineers, programmers, system architects, etc. 

How to Start

The Nika team comprises Research, Computing, Engineering and Geospatial focussed brilliant minds with a cumulative of 20+ years of experience in multiple geospatial system architecture expertise. This includes Enterprise Spatial Architect Leads, Senior Integration Engineers, Distributed System Engineers, Regulatory Compliance System Designers, PhDs and more.

As system integrators, we have a successful track record of guiding enterprises to make the jump from research to production, taking into full consideration business needs with technological capabilities.

Our diverse, cross-functional team can assess your geospatial models with technical and domain expertise, while our entrepreneurial experience makes the connection to commercialization and sustainable profit.

The valley of death between geospatial AI research and production applications represents both a massive missed opportunity and a significant business advantage for organizations that can bridge it successfully.

Get a free discovery call with our team to assess whether Nika can be your partner in bridging the gap between geospatial research and production.

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